| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.70% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1164 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 57.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1164 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "fractured" | | 1 | "flicker" | | 2 | "etched" | | 3 | "pulsed" | | 4 | "shimmered" | | 5 | "porcelain" | | 6 | "dancing" | | 7 | "flickered" | | 8 | "trembled" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 163 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 163 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 175 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 15 | | markdownWords | 37 | | totalWords | 1154 | | ratio | 0.032 | | matches | | 0 | "Keep moving. He’s turning." | | 1 | "Tomás Herrera." | | 2 | "No. He’s not." | | 3 | "off the map" | | 4 | "wrong" | | 5 | "clean" | | 6 | "Morris, if you're down there, I'm coming." | | 7 | "remembered" | | 8 | "\"The Veil Market moves, but its heart remains.\"" | | 9 | "He knows." | | 10 | "click" | | 11 | "human" | | 12 | "changed" | | 13 | "ethical" | | 14 | "pushing" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.80% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 996 | | uniqueNames | 12 | | maxNameDensity | 1 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Soho | 1 | | Wanted | 1 | | Underground | 1 | | Morris | 5 | | Glock | 1 | | Two | 1 | | Tomás | 3 | | Herrera | 6 | | Saint | 2 | | Christopher | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Wanted" | | 3 | "Morris" | | 4 | "Two" | | 5 | "Tomás" | | 6 | "Herrera" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | | globalScore | 0.998 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like blood in the rain" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1154 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 175 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 18.92 | | std | 18.59 | | cv | 0.982 | | sampleLengths | | 0 | 61 | | 1 | 4 | | 2 | 75 | | 3 | 37 | | 4 | 3 | | 5 | 23 | | 6 | 67 | | 7 | 25 | | 8 | 25 | | 9 | 12 | | 10 | 35 | | 11 | 26 | | 12 | 22 | | 13 | 2 | | 14 | 60 | | 15 | 28 | | 16 | 39 | | 17 | 16 | | 18 | 36 | | 19 | 29 | | 20 | 2 | | 21 | 36 | | 22 | 48 | | 23 | 27 | | 24 | 18 | | 25 | 6 | | 26 | 14 | | 27 | 13 | | 28 | 8 | | 29 | 3 | | 30 | 42 | | 31 | 3 | | 32 | 6 | | 33 | 11 | | 34 | 3 | | 35 | 2 | | 36 | 6 | | 37 | 4 | | 38 | 58 | | 39 | 9 | | 40 | 21 | | 41 | 5 | | 42 | 51 | | 43 | 6 | | 44 | 15 | | 45 | 17 | | 46 | 4 | | 47 | 8 | | 48 | 6 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 163 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 182 | | matches | | 0 | "wasn’t trying" | | 1 | "was *pushing" |
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| 44.90% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 175 | | ratio | 0.034 | | matches | | 0 | "Boots splashed through oily puddles reflecting the fractured neon glow above—pink, sickly green, a pulsing red that looked like blood in the rain." | | 1 | "*Tomás Herrera.* Wanted in three cases involving unsanctioned medical procedures—on patients later found with organs drained of something that wasn’t blood." | | 2 | "A flicker of movement beneath the archway leading to a disused Underground entrance—chained shut, rusted, but the padlock hung open." | | 3 | "And a smell—incense, yes, but underneath it, copper and ozone." | | 4 | "Faint light pulsed up from below—golden, warm, impossibly *clean* in this grime." | | 5 | "Instead, he slipped the vial into his coat, nodded once at the stall owner—a man with skin like cracked porcelain—and walked deeper into the market." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1005 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.028855721393034824 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006965174129353234 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 175 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 175 | | mean | 6.59 | | std | 5.53 | | cv | 0.839 | | sampleLengths | | 0 | 24 | | 1 | 11 | | 2 | 3 | | 3 | 23 | | 4 | 2 | | 5 | 2 | | 6 | 20 | | 7 | 11 | | 8 | 6 | | 9 | 21 | | 10 | 12 | | 11 | 5 | | 12 | 4 | | 13 | 9 | | 14 | 6 | | 15 | 5 | | 16 | 4 | | 17 | 6 | | 18 | 3 | | 19 | 1 | | 20 | 2 | | 21 | 20 | | 22 | 2 | | 23 | 1 | | 24 | 2 | | 25 | 2 | | 26 | 2 | | 27 | 2 | | 28 | 14 | | 29 | 2 | | 30 | 1 | | 31 | 8 | | 32 | 4 | | 33 | 8 | | 34 | 7 | | 35 | 15 | | 36 | 8 | | 37 | 1 | | 38 | 1 | | 39 | 5 | | 40 | 7 | | 41 | 3 | | 42 | 4 | | 43 | 4 | | 44 | 2 | | 45 | 1 | | 46 | 4 | | 47 | 10 | | 48 | 5 | | 49 | 3 |
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| 65.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.42857142857142855 | | totalSentences | 175 | | uniqueOpeners | 75 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 126 | | matches | | 0 | "Somewhere above, a cat yowled." | | 1 | "Illegally obtained from Morris’s effects." | | 2 | "Instead, he slipped the vial" | | 3 | "Then the stone slid aside," | | 4 | "Then she remembered the token." |
| | ratio | 0.04 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 126 | | matches | | 0 | "She didn’t slow." | | 1 | "He’d slipped out the back" | | 2 | "She’d seen the flash of" | | 3 | "She rounded the corner." | | 4 | "He was gone." | | 5 | "She pulled the token from" | | 6 | "She hadn’t known what it" | | 7 | "She did now." | | 8 | "She dropped to the arch’s" | | 9 | "She gripped her holstered Glock" | | 10 | "Her left brushed the worn" | | 11 | "She flattened against the wall." | | 12 | "His Saint Christopher medallion caught" | | 13 | "She needed eyes on him." | | 14 | "She edged forward." | | 15 | "He turned slowly." | | 16 | "He didn’t bolt." | | 17 | "Her boots silent now on" | | 18 | "She passed a cage of" | | 19 | "He stepped through." |
| | ratio | 0.246 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 88 | | totalSentences | 126 | | matches | | 0 | "The blade of the flashlight" | | 1 | "Detective Harlow Quinn landed hard," | | 2 | "She didn’t slow." | | 3 | "Boots splashed through oily puddles" | | 4 | "He’d slipped out the back" | | 5 | "She’d seen the flash of" | | 6 | "*Tomás Herrera.* Wanted in three" | | 7 | "She rounded the corner." | | 8 | "The alley narrowed, walls closing" | | 9 | "Trash bins overflowed with rot-scented" | | 10 | "Quinn reached the end." | | 11 | "A service road, cobbled and" | | 12 | "He was gone." | | 13 | "A flicker of movement beneath" | | 14 | "The case stayed sealed." | | 15 | "Orders came down from levels" | | 16 | "She pulled the token from" | | 17 | "She hadn’t known what it" | | 18 | "She did now." | | 19 | "Quinn snapped the torch off." |
| | ratio | 0.698 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 126 | | matches | (empty) | | ratio | 0 | |
| 68.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 3 | | matches | | 0 | "*Tomás Herrera.* Wanted in three cases involving unsanctioned medical procedures—on patients later found with organs drained of something that wasn’t blood." | | 1 | "Vendors hawked bottled shadows, jars of flickering starlight, and blades that shimmered like heat haze." | | 2 | "A book vendor sold leather-bound volumes that whispered when touched." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "he whispered (whisper)" |
| | dialogueSentences | 20 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0.5 | | effectiveRatio | 0.1 | |